Visible to the public Biblio

Found 15149 results

2022-09-29
Wei, Song, Zhang, Kun, Tu, Bibo.  2021.  Performance Impact of Host Kernel Page Table Isolation on Virtualized Servers. 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). :912–919.
As Meltdown mitigation, Kernel Page Table I solation (KPTI) was merged into Linux kernel mainline, and the performance impact is significant on x86 processors. Most of the previous work focuses on how KPTI affects Linux kernel performance within the scope of virtual machines or physical machines on x86. However, whether host KPTI affects virtual machines has not been well studied. What's more, there is relatively little research on ARM CPUs. This paper presents an in-depth study of how KPTI on the host affects the virtualized server performance and compares ARMv8 and x86. We first run several application benchmarks to demonstrate the performance impact does exist. The reason is that with a para-virtual I/O scheme, guest offloads I/O requests to the host side, which may incur user/kernel transitions. For the network I/O, when using QEMU as the back-end device, we saw a 1.7% and 5.5% slowdown on ARMv8 and x86, respectively. vhost and vhost-user, originally proposed to optimize performance, inadvertently mitigate the performance impact introduced by host KPTI. For CPU and memory-intensive benchmarks, the performance impact is trivial. We also find that virtual machines on ARMv8 are less affected by KPTI. To diagnose the root cause, we port HyperBench to the ARM virtualization platform. The final results show that swapping the translation table pointer register on ARMv8 is about 3.5x faster than x86. Our findings have significant implications for tuning the x86 virtualization platform's performance and helping ARMv8 administrators enable KPTI with confidence.
Suresh, V., Ramesh, M.K., Shadruddin, Sheikh, Paul, Tapobrata, Bhattacharya, Anirban, Ahmad, Abrar.  2021.  Design and Application of Converged Infrastructure through Virtualization Technology in Grid Operation Control Center in North Eastern Region of India. 2020 3rd International Conference on Energy, Power and Environment: Towards Clean Energy Technologies. :1–5.
Modern day grid operation requires multiple interlinked applications and many automated processes at control center for monitoring and operation of grid. Information technology integrated with operational technology plays a critical role in grid operation. Computing resource requirements of these software applications varies widely and includes high processing applications, high Input/Output (I/O) sensitive applications and applications with low resource requirements. Present day grid operation control center uses various applications for load despatch schedule management, various real-time analytics & optimization applications, post despatch analysis and reporting applications etc. These applications are integrated with Operational Technology (OT) like Data acquisition system / Energy management system (SCADA/EMS), Wide Area Measurement System (WAMS) etc. This paper discusses various design considerations and implementation of converged infrastructure through virtualization technology by consolidation of servers and storages using multi-cluster approach to meet high availability requirement of the applications and achieve desired objectives of grid control center of north eastern region in India. The process involves weighing benefits of different architecture solution, grouping of application hosts, making multiple clusters with reliability and security considerations, and designing suitable infrastructure to meet all end objectives. Reliability, enhanced resource utilization, economic factors, storage and physical node selection, integration issues with OT systems and optimization of cost are the prime design considerations. Modalities adopted to minimize downtime of critical systems for grid operation during migration from the existing infrastructure and integration with OT systems of North Eastern Regional Load Despatch Center are also elaborated in this paper.
Zhang, Zhengjun, Liu, Yanqiang, Chen, Jiangtao, Qi, Zhengwei, Zhang, Yifeng, Liu, Huai.  2021.  Performance Analysis of Open-Source Hypervisors for Automotive Systems. 2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS). :530–537.
Nowadays, automotive products are intelligence intensive and thus inevitably handle multiple functionalities under the current high-speed networking environment. The embedded virtualization has high potentials in the automotive industry, thanks to its advantages in function integration, resource utilization, and security. The invention of ARM virtualization extensions has made it possible to run open-source hypervisors, such as Xen and KVM, for embedded applications. Nevertheless, there is little work to investigate the performance of these hypervisors on automotive platforms. This paper presents a detailed analysis of different types of open-source hypervisors that can be applied in the ARM platform. We carry out the virtualization performance experiment from the perspectives of CPU, memory, file I/O, and some OS operation performance on Xen and Jailhouse. A series of microbenchmark programs have been designed, specifically to evaluate the real-time performance of various hypervisors and the relevant overhead. Compared with Xen, Jailhouse has better latency performance, stable latency, and little interference jitter. The performance experiment results help us summarize the advantages and disadvantages of these hypervisors in automotive applications.
Duman, Atahan, Sogukpinar, Ibrahim.  2021.  Deep Learning Based Event Correlation Analysis in Information Systems. 2021 6th International Conference on Computer Science and Engineering (UBMK). :209–214.
Information systems and applications provide indispensable services at every stage of life, enabling us to carry out our activities more effectively and efficiently. Today, information technology systems produce many alarm and event records. These produced records often have a relationship with each other, and when this relationship is captured correctly, many interruptions that will harm institutions can be prevented before they occur. For example, an increase in the disk I/O speed of a server or a problem may cause the business software running on that server to slow down and cause different results in this slowness. Here, an institution’s accurate analysis and management of all event records, and rule-based analysis of the resulting records in certain time periods and depending on certain rules will ensure efficient and effective management of millions of alarms. In addition, it will be possible to prevent possible problems by removing the relationships between events. Events that occur in IT systems are a kind of footprint. It is also vital to keep a record of the events in question, and when necessary, these event records can be analyzed to analyze the efficiency of the systems, harmful interferences, system failure tendency, etc. By understanding the undesirable situations such as taking the necessary precautions, possible losses can be prevented. In this study, the model developed for fault prediction in systems by performing event log analysis in information systems is explained and the experimental results obtained are given.
Tang, Houjun, Xie, Bing, Byna, Suren, Carns, Philip, Koziol, Quincey, Kannan, Sudarsun, Lofstead, Jay, Oral, Sarp.  2021.  SCTuner: An Autotuner Addressing Dynamic I/O Needs on Supercomputer I/O Subsystems. 2021 IEEE/ACM Sixth International Parallel Data Systems Workshop (PDSW). :29–34.
In high-performance computing (HPC), scientific applications often manage a massive amount of data using I/O libraries. These libraries provide convenient data model abstractions, help ensure data portability, and, most important, empower end users to improve I/O performance by tuning configurations across multiple layers of the HPC I/O stack. We propose SCTuner, an autotuner integrated within the I/O library itself to dynamically tune both the I/O library and the underlying I/O stack at application runtime. To this end, we introduce a statistical benchmarking method to profile the behaviors of individual supercomputer I/O subsystems with varied configurations across I/O layers. We use the benchmarking results as the built-in knowledge in SCTuner, implement an I/O pattern extractor, and plan to implement an online performance tuner as the SCTuner runtime. We conducted a benchmarking analysis on the Summit supercomputer and its GPFS file system Alpine. The preliminary results show that our method can effectively extract the consistent I/O behaviors of the target system under production load, building the base for I/O autotuning at application runtime.
Casini, Daniel, Biondi, Alessandro, Cicero, Giorgiomaria, Buttazzo, Giorgio.  2021.  Latency Analysis of I/O Virtualization Techniques in Hypervisor-Based Real-Time Systems. 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS). :306–319.
Nowadays, hypervisors are the standard solution to integrate different domains into a shared hardware platform, while providing safety, security, and predictability. To this end, a hypervisor virtualizes the physical platform and orchestrates the access to each component. When the system needs to comply with certification requirements for safety-critical systems, virtualization latencies need to be analytically bounded for providing off-line guarantees. This paper presents a detailed modeling of three I/O virtualization techniques, providing analytical bounds for each of them under different metrics. Experimental results compare the bounds for a case study and quantify the contribution due to different sources of delay.
Alsabbagh, Wael, Langendorfer, Peter.  2021.  A Fully-Blind False Data Injection on PROFINET I/O Systems. 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). :1–8.
This paper presents a fully blind false data injection (FDI) attack against an industrial field-bus i.e. PROFINET that is widely used in Siemens distributed Input/Output (I/O) systems. In contrast to the existing academic efforts in the research community which assume that an attacker is already familiar with the target system, and has a full knowledge of what is being transferred from the sensors or to the actuators in the remote I/O module, our attack overcomes these strong assumptions successfully. For a real scenario, we first sniff and capture real time data packets (PNIO-RT) that are exchanged between the IO-Controller and the IO-Device. Based on the collected data, we create an I/O database that is utilized to replace the correct data with false one automatically and online. Our full attack-chain is implemented on a real industrial setting based on Siemens devices, and tested for two scenarios. In the first one, we manipulate the data that represents the actual sensor readings sent from the IO-Device to the IO-Controller, whereas in the second scenario we aim at manipulating the data that represents the actuator values sent from the IO-Controller to the IO-Device. Our results show that compromising PROFINET I/O systems in the both tested scenarios is feasible, and the physical process to be controlled is affected. Eventually we suggest some possible mitigation solutions to secure our systems from such threats.
Yu, Zaifu, Shang, Wenqian, Lin, Weiguo, Huang, Wei.  2021.  A Collaborative Filtering Model for Link Prediction of Fusion Knowledge Graph. 2021 21st ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter). :33–38.
In order to solve the problem that collaborative filtering recommendation algorithm completely depends on the interactive behavior information of users while ignoring the correlation information between items, this paper introduces a link prediction algorithm based on knowledge graph to integrate ItemCF algorithm. Through the linear weighted fusion of the item similarity matrix obtained by the ItemCF algorithm and the item similarity matrix obtained by the link prediction algorithm, the new fusion matrix is then introduced into ItemCF algorithm. The MovieLens-1M data set is used to verify the KGLP-ItemCF model proposed in this paper, and the experimental results show that the KGLP-ItemCF model effectively improves the precision, recall rate and F1 value. KGLP-ItemCF model effectively solves the problems of sparse data and over-reliance on user interaction information by introducing knowledge graph into ItemCF algorithm.
López-Aguilar, Pablo, Solanas, Agusti.  2021.  Human Susceptibility to Phishing Attacks Based on Personality Traits: The Role of Neuroticism. 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). :1363–1368.
The COVID19 pandemic situation has opened a wide range of opportunities for cyber-criminals, who take advantage of the anxiety generated and the time spent on the Internet, to undertake massive phishing campaigns. Although companies are adopting protective measures, the psychological traits of the victims are still considered from a very generic perspective. In particular, current literature determines that the model proposed in the Big-Five personality traits (i.e., Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) might play an important role in human behaviour to counter cybercrime. However, results do not provide unanimity regarding the correlation between phishing susceptibility and neuroticism. With the aim to understand this lack of consensus, this article provides a comprehensive literature review of papers extracted from relevant databases (IEEE Xplore, Scopus, ACM Digital Library, and Web of Science). Our results show that there is not a well-established psychological theory explaining the role of neuroticism in the phishing context. We sustain that non-representative samples and the lack of homogeneity amongst the studies might be the culprits behind this lack of consensus on the role of neuroticism on phishing susceptibility.
Johnson, Chelsea K., Gutzwiller, Robert S., Gervais, Joseph, Ferguson-Walter, Kimberly J..  2021.  Decision-Making Biases and Cyber Attackers. 2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW). :140–144.
Cyber security is reliant on the actions of both machine and human and remains a domain of importance and continual evolution. While the study of human behavior has grown, less attention has been paid to the adversarial operator. Cyber environments consist of complex and dynamic situations where decisions are made with incomplete information. In such scenarios people form strategies based on simplified models of the world and are often efficient and effective, yet may result in judgement or decision-making bias. In this paper, we examine an initial list of biases affecting adversarial cyber actors. We use subject matter experts to derive examples and demonstrate these biases likely exist, and play a role in how attackers operate.
Rodrigues, André Filipe, Monteiro, Bruno Miguel, Pedrosa, Isabel.  2021.  Cybersecurity risks : A behavioural approach through the influence of media and information literacy. 2021 16th Iberian Conference on Information Systems and Technologies (CISTI). :1–6.
The growing use of digital media has been accompanied by an increase of the risks associated with the use of information systems, notably cybersecurity risks. In turn, the increasing use of information systems has an impact on users' media and information literacy. This research aims to address the relationship between media and information literacy, and the adoption of risky cybersecurity behaviours. This approach will be carried out through the definition of a conceptual framework supported by a literature review, and a quantitative research of the relationships mentioned earlier considering a sample composed by students of a Higher Education Institution.
Rohan, Rohani, Funilkul, Suree, Pal, Debajyoti, Chutimaskul, Wichian.  2021.  Understanding of Human Factors in Cybersecurity: A Systematic Literature Review. 2021 International Conference on Computational Performance Evaluation (ComPE). :133–140.
Cybersecurity is paramount for all public and private sectors for protecting their information systems, data, and digital assets from cyber-attacks; thus, relying on technology-based protections alone will not achieve this goal. This work examines the role of human factors in cybersecurity by looking at the top-tier conference on Human Factors in Cybersecurity over the past 6 years. A total of 24 articles were selected for the final analysis. Findings show that most of the authors used a quantitative method, where survey was the most used tool for collecting the data, and less attention has been paid to the theoretical research. Besides, three types of users were identified: university-level users, organizational-level users, and unspecified users. Culture is another less investigated aspect, and the samples were biased towards the western community. Moreover, 17 human factors are identified; human awareness, privacy perception, trust perception, behavior, and capability are the top five among them. Also, new insights and recommendations are presented.
Michaelides, N. V..  2021.  Examining attitudes towards cybersecurity compliance through the lens of the psychological contract. Competitive Advantage in the Digital Economy (CADE 2021). 2021:99–104.
This research proposal defines the aim to explore the perceptions and experiences of employees through a global pandemic. In an exploratory qualitative study, utilising thematic analysis, the intention is to take an interpretivist position to examine participants' accounts of working from home during a pandemic, by looking through the psychological contract (Rousseau, 1996a) lens to better understand this phenomenon. This research serves to offer a potential line of enquiry when it comes to the human factors of cyber and information security behaviour and any themes which may overlap with psychological contract breaches (PCB). Previous research has suggested that the psychological contract can impact on employee commitment towards their organisations (Jabeen, Behery and Hossam, 2015), and employees' attitudes towards cyber security affect the frequency in engaging in risky online behaviours, (Hadlington, 2018), this study aims to draw out any themes around these areas through semi-structured interviews with employees in a global law firm.
Al-Alawi, Adel Ismail, Alsaad, Abdulla Jalal, AlAlawi, Ebtesam Ismaeel, Naser Al-Hadad, Ahmed Abdulla.  2021.  The Analysis of Human Attitude toward Cybersecurity Information Sharing. 2021 International Conference on Decision Aid Sciences and Application (DASA). :947–956.
Over the years, human errors have been identified as one of the most critical factors impacting cybersecurity in an organization that has had a substantial impact. The research uses recent articles published on human resources and information cybersecurity. This research focuses on the vulnerabilities and the best solution to mitigate these threats based on literature review methodology. The study also focuses on identifying the human attitude and behavior towards cybersecurity and how that would impact the organization's financial impact. With the help of the Two-factor Taxonomy of the security behavior model developed in past research, the research aims to identify the best practices and compare the best practices with that of the attitude-behavior found and matched to the model. Finally, the study would compare the difference between best practices and the current practices from the model. This would help provide the organization with specific recommendations that would help change their attitude and behavior towards cybersecurity and ensure the organization is not fearful of the cyber threat of human error threat.
Ferguson-Walter, Kimberly J., Gutzwiller, Robert S., Scott, Dakota D., Johnson, Craig J..  2021.  Oppositional Human Factors in Cybersecurity: A Preliminary Analysis of Affective States. 2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW). :153–158.
The need for cyber defense research is growing as more cyber-attacks are directed at critical infrastructure and other sensitive networks. Traditionally, the focus has been on hardening system defenses. However, other techniques are being explored including cyber and psychological deception which aim to negatively impact the cognitive and emotional state of cyber attackers directly through the manipulation of network characteristics. In this study, we present a preliminary analysis of survey data collected following a controlled experiment in which over 130 professional red teamers participated in a network penetration task that included cyber deception and psychological deception manipulations [7]. Thematic and inductive analysis of previously un-analyzed open-ended survey responses revealed factors associated with affective states. These preliminary results are a first step in our analysis efforts and show that there are potentially several distinct dimensions of cyber-behavior that induce negative affective states in cyber attackers, which may serve as potential avenues for supplementing traditional cyber defense strategies.
Scott, Jasmine, Kyobe, Michael.  2021.  Trends in Cybersecurity Management Issues Related to Human Behaviour and Machine Learning. 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET). :1–8.
The number of organisational cybersecurity threats continues to increase every year as technology advances. All too often, organisations assume that implementing systems security measures like firewalls and anti-virus software will eradicate cyber threats. However, even the most robust security systems are vulnerable to threats. As advanced as machine learning cybersecurity technology is becoming, it cannot be solely relied upon to solve cyber threats. There are other forces that contribute to these threats that are many-a-times out of an organisation's control i.e., human behaviour. This research article aims to create an understanding of the trends in key cybersecurity management issues that have developed in the past five years in relation to human behaviour and machine learning. The methodology adopted to guide the synthesis of this review was a systematic literature review. The guidelines for conducting the review are presented in the review approach. The key cybersecurity management issues highlighted by the research includes risky security behaviours demonstrated by employees, social engineering, the current limitations present in machine learning insider threat detection, machine learning enhanced cyber threats, and the underinvestment challenges faced in the cybersecurity domain.
2022-09-20
Dong, Xingbo, Jin, Zhe, Zhao, Leshan, Guo, Zhenhua.  2021.  BioCanCrypto: An LDPC Coded Bio-Cryptosystem on Fingerprint Cancellable Template. 2021 IEEE International Joint Conference on Biometrics (IJCB). :1—8.
Biometrics as a means of personal authentication has demonstrated strong viability in the past decade. However, directly deriving a unique cryptographic key from biometric data is a non-trivial task due to the fact that biometric data is usually noisy and presents large intra-class variations. Moreover, biometric data is permanently associated with the user, which leads to security and privacy issues. Cancellable biometrics and bio-cryptosystem are two main branches to address those issues, yet both approaches fall short in terms of accuracy performance, security, and privacy. In this paper, we propose a Bio-Crypto system on fingerprint Cancellable template (Bio-CanCrypto), which bridges cancellable biometrics and bio-cryptosystem to achieve a middle-ground for alleviating the limitations of both. Specifically, a cancellable transformation is applied on a fixed-length fingerprint feature vector to generate cancellable templates. Next, an LDPC coding mechanism is introduced into a reusable fuzzy extractor scheme and used to extract the stable cryptographic key from the generated cancellable templates. The proposed system can achieve both cancellability and reusability in one scheme. Experiments are conducted on a public fingerprint dataset, i.e., FVC2002. The results demonstrate that the proposed LDPC coded reusable fuzzy extractor is effective and promising.
Bentahar, Atef, Meraoumia, Abdallah, Bendjenna, Hakim, Chitroub, Salim, Zeroual, Abdelhakim.  2021.  Eigen-Fingerprints-Based Remote Authentication Cryptosystem. 2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI). :1—6.
Nowadays, biometric is a most technique to authenticate /identify human been, because its resistance against theft, loss or forgetfulness. However, biometric is subject to different transmission attacks. Today, the protection of the sensitive biometric information is a big challenge, especially in current wireless networks such as internet of things where the transmitted data is easy to sniffer. For that, this paper proposes an Eigens-Fingerprint-based biometric cryptosystem, where the biometric feature vectors are extracted by the Principal Component Analysis technique with an appropriate quantification. The key-binding principle incorporated with bit-wise and byte-wise correcting code is used for encrypting data and sharing key. Several recognition rates and computation time are used to evaluate the proposed system. The findings show that the proposed cryptosystem achieves a high security without decreasing the accuracy.
Cooley, Rafer, Cutshaw, Michael, Wolf, Shaya, Foster, Rita, Haile, Jed, Borowczak, Mike.  2021.  Comparing Ransomware using TLSH and @DisCo Analysis Frameworks. 2021 IEEE International Conference on Big Data (Big Data). :2084—2091.
Modern malware indicators utilized by the current top threat feeds are easily bypassed and generated through enigmatic methods, leading to a lack of detection capabilities for cyber defenders. Static hash-based algorithms such as MD5 or SHA generate indicators that are rendered obsolete by modifying a single byte of the source file. Conversely, fuzzy hash-based algorithms such as SSDEEP and TLSH are more robust to alterations of source information; however, these methods often utilize context boundaries that are hard to define or not based on meaningful information. In previous work, a custom binary analysis tool was created called @DisCo. In this study, four current ransomware campaigns were analyzed using TLSH fuzzy hashing and the @DisCo tool. While TLSH works on the binary level of the entire program, @DisCo works at an intermediate function level. The results from each analysis method were compared to provide validation between the two as well as introduce a narrative for using combinations of these types of methods for the creation of stronger indicators of compromise.
Thao Nguyen, Thi Ai, Dang, Tran Khanh, Nguyen, Dinh Thanh.  2021.  Non-Invertibility for Random Projection based Biometric Template Protection Scheme. 2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM). :1—8.
Nowadays, biometric-based authentication systems are widely used. This fact has led to increased attacks on biometric data of users. Therefore, biometric template protection is sure to keep the attention of researchers for the security of the authentication systems. Many previous works proposed the biometric template protection schemes by transforming the original biometric data into a secure domain, or establishing a cryptographic key with the use of biometric data. The main purpose was that fulfill the all three requirements: cancelability, security, and performance as many as possible. In this paper, using random projection merged with fuzzy commitment, we will introduce a hybrid scheme of biometric template protection. We try to limit their own drawbacks and take full advantages of these techniques at the same time. In addition, an analysis of non-invertibility property will be exercised with regards to the use of random projection aiming at enhancing the security of the system while preserving the discriminability of the original biometric template.
Sreemol, R, Santosh Kumar, M B, Sreekumar, A.  2021.  Improvement of Security in Multi-Biometric Cryptosystem by Modulus Fuzzy Vault Algorithm. 2021 International Conference on Advances in Computing and Communications (ICACC). :1—7.
Numerous prevalent techniques build a Multi-Modal Biometric (MMB) system that struggles in offering security and also revocability onto the templates. This work proffered a MMB system centred on the Modulus Fuzzy Vault (MFV) aimed at resolving these issues. The methodology proposed includes Fingerprint (FP), Palmprint (PP), Ear and also Retina images. Utilizing the Boosted Double Plateau Histogram Equalization (BDPHE) technique, all images are improved. Aimed at removing the unnecessary things as of the ear and the blood vessels are segmented as of the retina images utilizing the Modified Balanced Iterative Reducing and Clustering using Hierarchy (MBIRCH) technique. Next, the input traits features are extracted; then the essential features are chosen as of the features extracted utilizing the Bidirectional Deer Hunting optimization Algorithm (BDHOA). The features chosen are merged utilizing the Normalized Feature Level and Score Level (NFLSL) fusion. The features fused are saved securely utilizing Modulus Fuzzy Vault. Upto fusion, the procedure is repeated aimed at the query image template. Next, the de-Fuzzy Vault procedure is executed aimed at the query template, and then the key is detached by matching the query template’s and input biometric template features. The key separated is analogized with the threshold that categorizes the user as genuine or else imposter. The proposed BDPHE and also MFV techniques function efficiently than the existent techniques.
Korenda, Ashwija Reddy, Afghah, Fatemeh, Razi, Abolfazl, Cambou, Bertrand, Begay, Taylor.  2021.  Fuzzy Key Generator Design using ReRAM-Based Physically Unclonable Functions. 2021 IEEE Physical Assurance and Inspection of Electronics (PAINE). :1—7.
Physical unclonable functions (PUFs) are used to create unique device identifiers from their inherent fabrication variability. Unstable readings and variation of the PUF response over time are key issues that limit the applicability of PUFs in real-world systems. In this project, we developed a fuzzy extractor (FE) to generate robust cryptographic keys from ReRAM-based PUFs. We tested the efficiency of the proposed FE using BCH and Polar error correction codes. We use ReRAM-based PUFs operating in pre-forming range to generate binary cryptographic keys at ultra-low power with an objective of tamper sensitivity. We investigate the performance of the proposed FE with real data using the reading of the resistance of pre-formed ReRAM cells under various noise conditions. The results show a bit error rate (BER) in the range of 10−5 for the Polar-codes based method when 10% of the ReRAM cell array is erroneous at Signal to Noise Ratio (SNR) of 20dB.This error rate is achieved by using helper data length of 512 bits for a 256 bit cryptographic key. Our method uses a 2:1 ratio for helper data and key, much lower than the majority of previously reported methods. This property makes our method more robust against helper data attacks.
Simjanović, Dušan J., Milošević, Dušan M., Milošević, Mimica R..  2021.  Fuzzy AHP based Ranking of Cryptography Indicators. 2021 15th International Conference on Advanced Technologies, Systems and Services in Telecommunications (℡SIKS). :237—240.
The progression of cryptographic attacks in the ICT era doubtless leads to the development of new cryptographic algorithms and assessment, and evaluation of the existing ones. In this paper, the artificial intelligence application, through the fuzzy analytic hierarchy process (FAHP) implementation, is used to rank criteria and sub-criteria on which the algorithms are based to determine the most promising criteria and optimize their use. Out of fifteen criteria, security soundness, robustness and hardware failure distinguished as significant ones.
Shaomei, Lv, Xiangyan, Zeng, Long, Huang, Lan, Wu, Wei, Jiang.  2021.  Passenger Volume Interval Prediction based on MTIGM (1,1) and BP Neural Network. 2021 33rd Chinese Control and Decision Conference (CCDC). :6013—6018.
The ternary interval number contains more comprehensive information than the exact number, and the prediction of the ternary interval number is more conducive to intelligent decision-making. In order to reduce the overfitting problem of the neural network model, a combination prediction method of the BP neural network and the matrix GM (1, 1) model for the ternary interval number sequence is proposed in the paper, and based on the proposed method to predict the passenger volume. The matrix grey model for the ternary interval number sequence (MTIGM (1, 1)) can stably predict the overall development trend of a time series. Considering the integrity of interval numbers, the BP neural network model is established by combining the lower, middle and upper boundary points of the ternary interval numbers. The combined weights of MTIGM (1, 1) and the BP neural network are determined based on the grey relational degree. The combined method is used to predict the total passenger volume and railway passenger volume of China, and the prediction effect is better than MTIGM (1, 1) and BP neural network.
Wang, Xuelei, Fidge, Colin, Nourbakhsh, Ghavameddin, Foo, Ernest, Jadidi, Zahra, Li, Calvin.  2021.  Feature Selection for Precise Anomaly Detection in Substation Automation Systems. 2021 13th IEEE PES Asia Pacific Power & Energy Engineering Conference (APPEEC). :1—6.
With the rapid advancement of the electrical grid, substation automation systems (SASs) have been developing continuously. However, with the introduction of advanced features, such as remote control, potential cyber security threats in SASs are also increased. Additionally, crucial components in SASs, such as protection relays, usually come from third-party vendors and may not be fully trusted. Untrusted devices may stealthily perform harmful or unauthorised behaviours which could compromise or damage SASs, and therefore, bring adverse impacts to the primary plant. Thus, it is necessary to detect abnormal behaviours from an untrusted device before it brings about catastrophic impacts. Anomaly detection techniques are suitable to detect anomalies in SASs as they only bring minimal side-effects to normal system operations. Many researchers have developed various machine learning algorithms and mathematical models to improve the accuracy of anomaly detection. However, without prudent feature selection, it is difficult to achieve high accuracy when detecting attacks launched from internal trusted networks, especially for stealthy message modification attacks which only modify message payloads slightly and imitate patterns of benign behaviours. Therefore, this paper presents choices of features which improve the accuracy of anomaly detection within SASs, especially for detecting “stealthy” attacks. By including two additional features, Boolean control data from message payloads and physical values from sensors, our method improved the accuracy of anomaly detection by decreasing the false-negative rate from 25% to 5% approximately.